MRI lesion size is more important than the number of positive biopsy cores in predicting adverse features and recurrence after radical prostatectomy: implications for active surveillance criteria in intermediate-risk patients

Quantified treatment effect at the individual level is more indicative for personalized radical prostatectomy recommendation: implications for prostate cancer treatment using deep learning

BACKGROUND

There are potential uncertainties and overtreatment existing in radical prostatectomy (RP) for prostate cancer (PCa) patients, thus identifying optimal candidates is quite important.

PURPOSE

This study aims to establish a novel causal inference deep learning (DL) model to discern whether a patient can benefit more from RP and to identify heterogeneity in treatment responses among PCa patients.

METHODS

We introduce the Self-Normalizing Balanced individual treatment effect for survival data (SNB). Six models were trained to make individualized treatment recommendations for PCa patients. Inverse probability treatment weighting (IPTW) was used to avoid treatment selection bias.

RESULTS

35,236 patients were included. Patients whose actual treatment was consistent with SNB recommendations had better survival outcomes than those who were inconsistent (multivariate hazard ratio (HR): 0.76, 95% confidence interval (CI), 0.64-0.92; IPTW-adjusted HR: 0.77, 95% CI, 0.61-0.95; risk difference (RD): 3.80, 95% CI, 2.48-5.11; IPTW-adjusted RD: 2.17, 95% CI, 0.92-3.35; the difference in restricted mean survival time (dRMST): 3.81, 95% CI, 2.66-4.85; IPTW-adjusted dRMST: 3.23, 95% CI, 2.06-4.45). Keeping other covariates unchanged, patients with 1 ng/mL increase in PSA levels received RP caused 1.77 months increase in the time to 90% mortality, and the similar results could be found in age, Gleason score, tumor size, TNM stages, and metastasis status.

CONCLUSIONS

Our highly interpretable and reliable DL model (SNB) may identify patients with PCa who could benefit from RP, outperforming other models and clinical guidelines. Additionally, the DL-based treatment guidelines obtained can provide priori evidence for subsequent studies.

Likelihood of sampling prostate cancer at systematic biopsy as a function of gland volume and number of cores

BACKGROUND

Pre-biopsy multiparametric magnetic resonance imaging (mpMRI) of the prostate is used to conduct targeted prostate biopsy (TB), guided by ultrasound and registered (fused) to the MRI. Systematic biopsy (SB) continues to be used together with TB or in mpMRI-negative patients. There is insufficient evidence on how to use SB to inform clinical decision-making in the mpMRI era. The purpose of this study was to estimate the effect of prostate volume and number of SB cores on sampling clinically significant prostate cancer (csPCa) using a simulation method based on clinical data.

METHODS

SBs were simulated using data from 42 patients enrolled in a transrectal ultrasound robot-assisted biopsy trial. Linear mixed models were used to examine the relationship between the number of SB cores and prostate volume on 1) clinically significant cancer detection probability (csCDP) and 2) percent of mpMRI depicted regions of interest (ROIs) sampled with the SB.

RESULTS

Median values and interquartile range (IQR) were 47.16 cm3 (35.61-65.57) for prostate volume, 0.57 cm3 (0.39-0.83) for ROI volume, and 4.0 (2-4) for PI-RADS v2.1 scores on MRI. csCDP increased with the increasing number of simulated SB cores and decreased substantially with larger prostate volume. Similarly, the percent of ROIs sampled increased with the increasing number of simulated SB cores and was lower for prostate volumes ≥60 cm3 compared to glands <60 cm3.

CONCLUSIONS

The effect of the number of SBs performed on detecting csPCa varies largely with gland volume. The common 12-core SB can achieve adequate cancer detection and sampling of ROIs in smaller glands, but not in larger glands. In addition to TB or in mpMRI-negative patients, the number of SB cores can be adjusted to prostate volume. Performing 12-core SB alone in ≥60 cm3 glands results in inadequate sampling and potential PCa underdiagnosis.

Active surveillance in favorable intermediate-risk prostate cancer: A single-center experience

PURPOSE

To report the long-term oncological outcomes of active surveillance (AS) in selected patients with favorable intermediate-risk (IR) prostate cancer (PCa).

METHODS

A retrospective database review of two academic centers was conducted to identify favorable IR PCa patients initially managed by AS between 2014 and 2022. Favorable IR PCa was defined by the presence of one single element of IR disease (i.e., PSA 10-20ng/mL, Gleason Grade Group [GG] 2, or cT2b). All patients were diagnosed and followed up according to a contemporary scheme, including MRI and image-guided biopsies. The primary endpoint was metastasis-free survival.

RESULTS

A total of 57 patients met our inclusion criteria and the median follow-up was 56months. During follow-up, there were no cases of metastasis or death due to PCa, but 6 deaths due to competing causes. A total of 25 (44%) and 6 patients (11%) had definitive treatment and GG 3 reclassification during follow-up, respectively. In multivariable Cox hazard regression analysis, the risk of undergoing definitive treatment was significantly associated with PSA density>0.15 (HR: 4.82, 95% CI: 1.47 to 15; P=0.01) and PI-RADS 4-5 lesions on mpMRI (HR: 2.48, 95% CI: 1.06 to 5.19; P=0.006). Interestingly, tumor burden (P=0.3) and GG (P=0.7) on biopsy were not associated with definitive treatment.

CONCLUSIONS

AS is a safe and valuable strategy for well-selected patients with favorable IR prostate cancer, with excellent oncological outcomes after five years' follow-up.

LEVEL OF EVIDENCE: 4